CROI 2018 Abstract eBook

Abstract eBook

Poster Abstracts

Background: Phylodynamic assessment of HIV transmission clusters is essential for monitoring the HIV epidemic, better understanding of HIV transmission dynamics, and ultimately controlling the HIV epidemic. The phylogenetic structure of the HIV transmission network remains unknown. Methods: Near full-length HIV-1C sequences were obtained from blood specimens collected within four Botswana-Harvard AIDS Institute Partnership studies. The genotyping density (the number of analyzed HIV genomes as a proportion of the estimated total number of HIV-infected residents in the targeted area) was under 20%, which we consider as a low sampling density. A total of 3,031 HIV-1C sequences originated from the South (55%), East (30%) and North (15%) of Botswana. Near full-length genome HIV sequences were generated by Sanger sequencing (n=273) and next generation sequencing (n=2,758). Phylogenetic relatedness among analyzed viral sequences was estimated by maximum likelihood using RAxML v.8 and the GTR+Γ4+Ι model. Results: We defined an HIV cluster as a viral lineage that gives rise to a monophyletic subtree of the overall phylogeny with bootstrap support of splits 0.80 and median pairwised distance10th quartile of the overall distribution of pairwise distances. We identified 472 phylogenetically distinct HIV-1C lineages circulating in Botswana and 402 of them had predominantly (75%) Botswana sequences. The identified HIV clusters had from 2 to 22 members. The proportion of local viral lineages (community-unique) seen in a single community was 28% (112 of 402). Among HIV-1C lineages spread across multiple communities, 47% (188 of 402) were found in two communities and 25% (102 of 402) were spread across 3 communities. Regional analysis (South vs. East vs. North) demonstrated that 60% (243 of 402) of viral lineages were identified exclusively in the South, East, or North of the country. Among lineages seen in two communities, 99 were identified within, while 89 were spread between geographic regions. Conclusion: The study revealed an HIV-1C transmission network with a complex structure. A substantial number of circulating phylogenetically distinct HIV-1C lineages were identified, although the genotyping density was relatively low. Twenty-eight percent of viral lineages were local (community-unique), while about half of the identified lineages spread across two communities. The distribution of HIV-1C lineages within vs. between geographic regions split 60% vs. 40%. 951 HIV-1 GENETIC DIVERSITY AND TRANSMISSION DYNAMICS IN FISHING COMMUNITIES OF UGANDA Nicholas Bbosa 1 , Deogratius Ssemwanga 1 , Jesus F. Salazar-Gonzalez 1 , Rebecca N. Nsubuga 1 , Maria Nannyonjo 1 , Janet Seeley 1 , Noah Kiwanuka 2 , Bernard Bagaya 2 , Gonzalo Yebra 3 , Andrew Leigh Brown 3 , Pontiano Kaleebu 1 1 MRC/UVRI Research Unit on AIDS, Entebbe, Uganda, 2 Makerere University College of Health Sciences, Kampala, Uganda, 3 University of Edinburgh, Edinburgh, UK Background: Although fishing communities (FCs) in Uganda are disproportionately affected by HIV-1 (prevalence of 29% and incidence of 6/100 PYAR) relative to the general population (GP), the patterns of viral transmission in this group are not completely understood to guide the implementation of targeted interventions aimed at controlling disease spread. Phylogenetic methods were used to test the hypothesis that HIV-1 transmissions in fishing villages are isolated from networks in the GP. Methods: In this cross-sectional study, we classified viral subtypes and used Bayesian phylogenetic inference to analyze nucleotide sequences with socio- demographics to identify transmission networks and reconstruct the spatial- temporal dynamics of HIV-1 transmission in 8 FCs (n=255) and 2 neighboring GP cohorts (n=305). Time-resolved trees were generated in BEAST v1.8.3 for phylodynamic and phylogeographic analyses. Results: Subtype A1 was the prevalent subtype in both the FCs and GP (115, 45.1% and 177, 50.4% respectively) followed by subtype D (84, 32.9% and 121, 34.5%), A1/D recombinants (28, 11% and 37, 10.5%), other recombinants and minor subtypes. 31 linked pairs were found at a maximum genetic distance (GD) of 4.5%, 13 of these were closer than 1.5% but these were significantly more frequent in FCs (Table 1). Confirmation of recent HIV-1 transmission was obtained from phylodynamic analysis (average time to most recent common ancestor and sampling times=6mo). A significant positive relationship between GD and time since most recent common ancestor was observed in this population (r=0.7, p<0.05), but on an individual level this had low predictive power (positive predictive values at GD thresholds of 1.47 and 4.38 were 52.69% and 29.7% respectively). Phylogeographic analysis showed significant viral diffusion between FCs and the neighboring GP (BF>3) with stronger

949 PREDICTING HIV CLUSTER GROWTH USING PHYLODYNAMIC RECONSTRUCTION IN LOS ANGELES COUNTY Manon Ragonnet-Cronin 1 , Yunyin W. Hu 2 , Joel O. Wertheim 1 1 University of California San Diego, San Diego, CA, USA, 2 Los Angeles County Department of Health Services, Los Angeles, CA, USA Background: Genetic clustering approaches are increasingly adopted in Public Health practice to identify groups of HIV-infected individuals potentially arising from rapidly growing transmission clusters. Members of these clusters are candidates for targeted HIV and STD prevention activities such as early treatment initiation and linkage to, and reengagement in, care, and pre- exposure prophylaxis and partner services among at-risk partners. The goal of this study is to identify the most effective computational approaches to detect these rapidly growing clusters. Methods: The study utilized the earliest HIV pol sequences among 22,398 persons reported in Los Angeles County Molecular HIV Surveillance database from 2000 to 2016. We evaluated five approaches to characterize cluster growth: (i) number of newly identified cluster members in relation to the cluster size [relative growth], (ii) sigmoidal curve fitting, (iii) phylodynamic estimation of the change in effective population size, (iv) phylodynamic estimation of epidemic reproductive number, (v) randomly selected clusters. Clusters for each year in 2008-2015 were identified using HIV-TRACE (pairwise genetic distance threshold of 0.015 substitutions/site). The number of individuals added to the clusters, selected by each approach, over the subsequent 12 months was evaluated to determine the best method for predicting cluster growth. Results: Of 22,398 persons, 8,133 (36.3%) were linked in 1,722 clusters ranging from 2 to 116 individuals. All approaches predicted cluster growth better than the randommethod. On average, these four approaches identified a growth rate of 0.3 newly linked persons within 12 months, compared with 0.15 persons in the randommethod. Although both phylodynamic reconstruction methods could be used to impute non-tested/non-reported cases within a cluster, they did not perform better than relative cluster growth. Notably, phylodynamic analyses of clusters with fewer than 10 cases often failed to converge, (likely due to lack of signal). When we re-analyzed the network focusing only on large clusters (≥10 individuals), none of the methods performed significantly better than targeting large clusters at random. Conclusion: Past cluster growth is a reliable predictor of future growth. Cluster growth relative to cluster size was as predictive of future cluster growth as phylodynamic reconstruction and was much faster and more reliable to calculate. 950 ASSESSING HIV-1C TRANSMISSION NETWORK IN BOTSWANA AT LOW SAMPLING DENSITY Vlad Novitsky 1 , Melissa Zahralban-Steele 1 , Sikhulile Moyo 2 , Dorcas Maruapula 2 , Baitshepi Mokaleng 2 , Tapiwa Nkhisang 1 , Mary F. McLane 1 , Sally Madiba 1 , Erik van Widenfelt 2 , Tendani Gaolathe 2 , Etienne Kadima 2 , Shahin Lockman 1 , Joseph Makhema 2 , Simani Gaseitsiwe 2 , Max Essex 1 1 Harvard University, Cambridge, MA, USA, 2 Botswana Harvard AIDS Institute Partnership, Gabarone, Botswana

Poster Abstracts

CROI 2018 363